The assembly of DNA sequences de novo is fundamental to genomics research. It is the first of many steps toward elucidating and characterizing whole genomes. Downstream applications, including analysis of genomic variation between species, between or within individuals critically depend on robustly assembled sequences. In the span of a single decade, the sequence throughput of leading DNA sequencing instruments has increased drastically, and coupled with established and planned large-scale, personalized medicine initiatives to sequence genomes in the thousands and even millions, the development of efficient, scalable and accurate bioinformatics tools for producing high-quality reference draft genomes is timely. With ABySS 1.0, we originally showed that assembling the human genome using short 50-bp sequencing reads was possible by aggregating the half terabyte of compute memory needed over several computers using a standardized message-passing system (MPI). We present here its redesign, which departs from MPI and instead implements algorithms that employ a Bloom filter, a probabilistic data structure, to represent a de Bruijn graph and reduce memory requirements.

MotivationSequencing of human genomes is now routine, and assembly of shotgun reads is increasingly feasible. However, assemblies often fail to inform about chromosome-scale structure due to a lack of linkage information over long stretches of DNA—a shortcoming that is being addressed by new sequencing protocols, such as the GemCode and Chromium linked reads from 10 × Genomics.ResultsHere, we present ARCS, an application that utilizes the barcoding information contained in linked reads to further organize draft genomes into highly contiguous assemblies. We show how the contiguity of an ABySS H.sapiens genome assembly can be increased over six-fold, using moderate coverage (25-fold) Chromium data. We expect ARCS to have broad utility in harnessing the barcoding information contained in linked read data for connecting high-quality sequences in genome assembly drafts.Availability and implementation https://github.com/bcgsc/ARCS/ Supplementary information Supplementary data are available at Bioinformatics online.

The assembly of DNA sequences de novo is fundamental to genomics research. It is the first of many steps towards elucidating and characterizing whole genomes. Downstream applications, including analysis of genomic variation between species, between or within individuals critically depends on robustly assembled sequences. In the span of a single decade, the sequence throughput of leading DNA sequencing instruments has increased drastically, and coupled with established and planned large-scale, personalized medicine initiatives to sequence genomes in the thousands and even millions, the development of efficient, scalable and accurate bioinformatics tools for producing high-quality reference draft genomes is timely.With ABySS 1.0, we originally showed that assembling the human genome using short 50 bp sequencing reads was possible by aggregating the half terabyte of compute memory needed over several computers using a standardized messagepassing system (MPI). We present here its re-design, which departs from MPI and instead implements algorithms that employ a Bloom filter, a probabilistic data structure, to represent a de Bruijn graph and reduce memory requirements.We present assembly benchmarks of human Genome in a Bottle 250 bp Illumina paired-end and 6 kbp mate-pair libraries from a single individual, yielding a NG50 (NGA50) scaffold contiguity of 3.5 (3.0) Mbp using less than 35 GB of RAM, a modest memory requirement by today's standard that is often available on a single computer. We also investigate the use of BioNano Genomics and 10x Genomics' Chromium data to further improve the scaffold contiguity of this assembly to 42 (15) Mbp.

BackgroundGenome sequencing yields the sequence of many short snippets of DNA (reads) from a genome. Genome assembly attempts to reconstruct the original genome from which these reads were derived. This task is difficult due to gaps and errors in the sequencing data, repetitive sequence in the underlying genome, and heterozygosity. As a result, assembly errors are common. In the absence of a reference genome, these misassemblies may be identified by comparing the sequencing data to the assembly and looking for discrepancies between the two. Once identified, these misassemblies may be corrected, improving the quality of the assembled sequence. Although tools exist to identify and correct misassemblies using Illumina paired-end and mate-pair sequencing, no such tool yet exists that makes use of the long distance information of the large molecules provided by linked reads, such as those offered by the 10x Genomics Chromium platform. We have developed the tool Tigmint to address this gap.ResultsTo demonstrate the effectiveness of Tigmint, we applied it to assemblies of a human genome using short reads assembled with ABySS 2.0 and other assemblers. Tigmint reduced the number of misassemblies identified by QUAST in the ABySS assembly by 216 (27%). While scaffolding with ARCS alone more than doubled the scaffold NGA50 of the assembly from 3 to 8 Mbp, the combination of Tigmint and ARCS improved the scaffold NGA50 of the assembly over five-fold to 16.4 Mbp. This notable improvement in contiguity highlights the utility of assembly correction in refining assemblies. We demonstrate the utility of Tigmint in correcting the assemblies of multiple tools, as well as in using Chromium reads to correct and scaffold assemblies of long single-molecule sequencing.ConclusionsScaffolding an assembly that has been corrected with Tigmint yields a final assembly that is both more correct and substantially more contiguous than an assembly that has not been corrected. Using single-molecule sequencing in combination with linked reads enables a genome sequence assembly that achieves both a high sequence contiguity as well as high scaffold contiguity, a feat not currently achievable with either technology alone.

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Alignment-free classification tools have enabled high-throughput processing of sequencing data in many bioinformatics analysis pipelines primarily due to their computational efficiency. Originally k-mer based, such tools often lack sensitivity when faced with sequencing errors and polymorphisms. In response, some tools have been augmented with spaced seeds, which are capable of tolerating mismatches. However, spaced seeds have seen little practical use in classification because they bring increased computational and memory costs compared to methods that use k-mers. These limitations have also caused the design and length of practical spaced seeds to be constrained, since storing spaced seeds can be costly. To address these challenges, we have designed a probabilistic data structure called a multiindex Bloom Filter (miBF), which can store multiple spaced seed sequences with a low memory cost that remains static regardless of seed length or seed design. We formalize how to minimize the false-positive rate of miBFs when classifying sequences from multiple targets or references. Available within BioBloom Tools, we illustrate the utility of miBF in two use cases: read-binning for targeted assembly, and taxonomic read assignment. In our benchmarks, an analysis pipeline based on miBF shows higher sensitivity and specificity for read-binning than sequence alignment-based methods, also executing in less time. Similarly, for taxonomic classification, miBF enables higher sensitivity than a conventional spaced seed-based approach, while using half the memory and an order of magnitude less computational time.

Our PIP analysis suggests that CPNP was feasibly implemented, promoted a sustained utilization of proper feeding behaviours, and enhanced participation in the existing ENA programme. These findings provide a possible explanation to understanding CPNP's effectiveness.

Abstract. Sequencing of human genomes is now routine, and assembly of shotgun reads is increasingly feasible. However, assemblies often fail to inform about chromosome-scale structure due to lack of linkage information over long stretches of DNA -a shortcoming that is being addressed by new sequencing protocols, such as linked reads from 10X Genomics. Here we present ARCS, an application that utilizes the barcoding information contained in linked reads to further organize draft genomes into highly contiguous assemblies. We show how the contiguity of an ABySS H. sapiens genome assembly can be increased over six-fold using moderate coverage (25-fold) Chromium data. We expect ARCS to have broad utility in harnessing the barcoding information contained in Chromium data for connecting high-quality sequences in genome assembly drafts.